{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1801,
   "id": "547efae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>last_pay_time</th>\n",
       "      <th>pay_num</th>\n",
       "      <th>pay_times</th>\n",
       "      <th>last_month_traffic</th>\n",
       "      <th>local_trafffic_month</th>\n",
       "      <th>local_caller_time</th>\n",
       "      <th>service1_caller_time</th>\n",
       "      <th>service2_caller_time</th>\n",
       "      <th>online_time</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>1392.038508</td>\n",
       "      <td>108.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>564.366667</td>\n",
       "      <td>85</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62852.509718</td>\n",
       "      <td>240.100000</td>\n",
       "      <td>355.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-19 00:00:00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1935.242104</td>\n",
       "      <td>27.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.416667</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16 00:00:00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>37.336425</td>\n",
       "      <td>988.561075</td>\n",
       "      <td>89.900000</td>\n",
       "      <td>74.483333</td>\n",
       "      <td>121.833333</td>\n",
       "      <td>134</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-27 00:00:00</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>3305.741127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.100000</td>\n",
       "      <td>496.733333</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4qHSn3dkPzJTAjoG</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-22 00:00:00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>459.294048</td>\n",
       "      <td>218.003452</td>\n",
       "      <td>14.633333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.633333</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tXkjbzpTsZcxYPKG</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-11 00:00:00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.116667</td>\n",
       "      <td>136.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ro43b68MustgPyOR</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-28 00:00:00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>635.978400</td>\n",
       "      <td>250.883333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>310.733333</td>\n",
       "      <td>109</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18e2VC0IJ7SkcKzF</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:00:00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>22.655023</td>\n",
       "      <td>15.283333</td>\n",
       "      <td>41.900000</td>\n",
       "      <td>82.916667</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>YCEo95zSZ08IJ3PW</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25 00:00:00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7830.505474</td>\n",
       "      <td>33.733333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.833333</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  gender        last_pay_time  pay_num  pay_times  \\\n",
       "0  Gx4sJzcQog01UhZL       1  2016-06-26 00:00:00   300.04          2   \n",
       "1  kEXrhTiug93DIcLG       1  2016-06-26 00:00:00   300.00          3   \n",
       "2  AouXr0EOUtSRdiYK       2  2016-06-19 00:00:00    50.00          4   \n",
       "3  Yds7U30hnRZDiLtb       1  2016-06-16 00:00:00   100.00          1   \n",
       "4  OFDTSXrhN9Q2mbVw       1  2016-06-27 00:00:00  1000.03         12   \n",
       "5  4qHSn3dkPzJTAjoG       1  2016-06-22 00:00:00    30.00          1   \n",
       "6  tXkjbzpTsZcxYPKG       1  2016-06-11 00:00:00    50.00          1   \n",
       "7  ro43b68MustgPyOR       1  2016-06-28 00:00:00   200.00          2   \n",
       "8  18e2VC0IJ7SkcKzF       1  2016-06-12 00:00:00   200.00          2   \n",
       "9  YCEo95zSZ08IJ3PW       1  2016-06-25 00:00:00   120.00          2   \n",
       "\n",
       "   last_month_traffic  local_trafffic_month  local_caller_time  \\\n",
       "0         4096.000000           1392.038508         108.100000   \n",
       "1            0.000000          62852.509718         240.100000   \n",
       "2            0.000000           1935.242104          27.666667   \n",
       "3           37.336425            988.561075          89.900000   \n",
       "4         3305.741127              0.000000           0.000000   \n",
       "5          459.294048            218.003452          14.633333   \n",
       "6            0.000000              0.000000           6.116667   \n",
       "7         1024.000000            635.978400         250.883333   \n",
       "8          500.000000             22.655023          15.283333   \n",
       "9            0.000000           7830.505474          33.733333   \n",
       "\n",
       "   service1_caller_time  service2_caller_time  online_time  age  \n",
       "0              0.000000            564.366667           85   31  \n",
       "1            355.166667              0.000000           10   30  \n",
       "2              0.000000             98.416667           12   25  \n",
       "3             74.483333            121.833333          134   44  \n",
       "4             35.100000            496.733333           84   31  \n",
       "5              0.000000             14.633333           46   42  \n",
       "6            136.033333              0.000000           12   27  \n",
       "7              0.000000            310.733333          109   40  \n",
       "8             41.900000             82.916667           30   43  \n",
       "9              0.000000            101.833333            7   22  "
      ]
     },
     "execution_count": 1801,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "datafile = \"./RFM聚类分析.xlsx\"\n",
    "data = pd.read_excel(datafile)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1802,
   "id": "d5aeb612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 12)"
      ]
     },
     "execution_count": 1802,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1803,
   "id": "b79df2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   user_id               301 non-null    object \n",
      " 1   gender                301 non-null    int64  \n",
      " 2   last_pay_time         301 non-null    object \n",
      " 3   pay_num               301 non-null    float64\n",
      " 4   pay_times             301 non-null    int64  \n",
      " 5   last_month_traffic    301 non-null    float64\n",
      " 6   local_trafffic_month  301 non-null    float64\n",
      " 7   local_caller_time     301 non-null    float64\n",
      " 8   service1_caller_time  301 non-null    float64\n",
      " 9   service2_caller_time  301 non-null    float64\n",
      " 10  online_time           301 non-null    int64  \n",
      " 11  age                   301 non-null    int64  \n",
      "dtypes: float64(6), int64(4), object(2)\n",
      "memory usage: 28.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1804,
   "id": "a09d6e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>last_pay_time</th>\n",
       "      <th>pay_num</th>\n",
       "      <th>pay_times</th>\n",
       "      <th>last_month_traffic</th>\n",
       "      <th>local_trafffic_month</th>\n",
       "      <th>local_caller_time</th>\n",
       "      <th>service1_caller_time</th>\n",
       "      <th>service2_caller_time</th>\n",
       "      <th>online_time</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  gender  last_pay_time  pay_num  pay_times  last_month_traffic  \\\n",
       "0      False   False          False    False      False               False   \n",
       "1      False   False          False    False      False               False   \n",
       "2      False   False          False    False      False               False   \n",
       "3      False   False          False    False      False               False   \n",
       "4      False   False          False    False      False               False   \n",
       "..       ...     ...            ...      ...        ...                 ...   \n",
       "296    False   False          False    False      False               False   \n",
       "297    False   False          False    False      False               False   \n",
       "298    False   False          False    False      False               False   \n",
       "299    False   False          False    False      False               False   \n",
       "300    False   False          False    False      False               False   \n",
       "\n",
       "     local_trafffic_month  local_caller_time  service1_caller_time  \\\n",
       "0                   False              False                 False   \n",
       "1                   False              False                 False   \n",
       "2                   False              False                 False   \n",
       "3                   False              False                 False   \n",
       "4                   False              False                 False   \n",
       "..                    ...                ...                   ...   \n",
       "296                 False              False                 False   \n",
       "297                 False              False                 False   \n",
       "298                 False              False                 False   \n",
       "299                 False              False                 False   \n",
       "300                 False              False                 False   \n",
       "\n",
       "     service2_caller_time  online_time    age  \n",
       "0                   False        False  False  \n",
       "1                   False        False  False  \n",
       "2                   False        False  False  \n",
       "3                   False        False  False  \n",
       "4                   False        False  False  \n",
       "..                    ...          ...    ...  \n",
       "296                 False        False  False  \n",
       "297                 False        False  False  \n",
       "298                 False        False  False  \n",
       "299                 False        False  False  \n",
       "300                 False        False  False  \n",
       "\n",
       "[301 rows x 12 columns]"
      ]
     },
     "execution_count": 1804,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1805,
   "id": "f632d10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                 0\n",
       "gender                  0\n",
       "last_pay_time           0\n",
       "pay_num                 0\n",
       "pay_times               0\n",
       "last_month_traffic      0\n",
       "local_trafffic_month    0\n",
       "local_caller_time       0\n",
       "service1_caller_time    0\n",
       "service2_caller_time    0\n",
       "online_time             0\n",
       "age                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1805,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1806,
   "id": "b6204682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>pay_num</th>\n",
       "      <th>pay_times</th>\n",
       "      <th>last_month_traffic</th>\n",
       "      <th>local_trafffic_month</th>\n",
       "      <th>local_caller_time</th>\n",
       "      <th>service1_caller_time</th>\n",
       "      <th>service2_caller_time</th>\n",
       "      <th>online_time</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.199336</td>\n",
       "      <td>119.214518</td>\n",
       "      <td>1.990033</td>\n",
       "      <td>447.762999</td>\n",
       "      <td>6065.336874</td>\n",
       "      <td>47.652326</td>\n",
       "      <td>33.913234</td>\n",
       "      <td>95.810742</td>\n",
       "      <td>39.813953</td>\n",
       "      <td>31.215947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.496789</td>\n",
       "      <td>148.409316</td>\n",
       "      <td>1.519836</td>\n",
       "      <td>1122.077105</td>\n",
       "      <td>11290.348491</td>\n",
       "      <td>85.436413</td>\n",
       "      <td>81.736976</td>\n",
       "      <td>140.865125</td>\n",
       "      <td>44.629198</td>\n",
       "      <td>12.230149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.655023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1483.326315</td>\n",
       "      <td>7.883333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.566667</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>5810.480609</td>\n",
       "      <td>58.566667</td>\n",
       "      <td>26.733333</td>\n",
       "      <td>158.033333</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1477.600000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11264.000000</td>\n",
       "      <td>75701.775427</td>\n",
       "      <td>567.900000</td>\n",
       "      <td>675.650000</td>\n",
       "      <td>809.616667</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gender      pay_num   pay_times  last_month_traffic  \\\n",
       "count  301.000000   301.000000  301.000000          301.000000   \n",
       "mean     1.199336   119.214518    1.990033          447.762999   \n",
       "std      0.496789   148.409316    1.519836         1122.077105   \n",
       "min      0.000000     0.000000    0.000000            0.000000   \n",
       "25%      1.000000    42.000000    1.000000            0.000000   \n",
       "50%      1.000000    80.000000    2.000000            0.000000   \n",
       "75%      1.000000   150.000000    2.000000          500.000000   \n",
       "max      2.000000  1477.600000   12.000000        11264.000000   \n",
       "\n",
       "       local_trafffic_month  local_caller_time  service1_caller_time  \\\n",
       "count            301.000000         301.000000            301.000000   \n",
       "mean            6065.336874          47.652326             33.913234   \n",
       "std            11290.348491          85.436413             81.736976   \n",
       "min                0.000000           0.000000              0.000000   \n",
       "25%               22.655023           0.000000              0.000000   \n",
       "50%             1483.326315           7.883333              0.000000   \n",
       "75%             5810.480609          58.566667             26.733333   \n",
       "max            75701.775427         567.900000            675.650000   \n",
       "\n",
       "       service2_caller_time  online_time         age  \n",
       "count            301.000000   301.000000  301.000000  \n",
       "mean              95.810742    39.813953   31.215947  \n",
       "std              140.865125    44.629198   12.230149  \n",
       "min                0.000000     4.000000    0.000000  \n",
       "25%                0.000000    10.000000   24.000000  \n",
       "50%               28.566667    17.000000   30.000000  \n",
       "75%              158.033333    59.000000   39.000000  \n",
       "max              809.616667   249.000000   70.000000  "
      ]
     },
     "execution_count": 1806,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1807,
   "id": "63724cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                 False\n",
       "gender                   True\n",
       "last_pay_time           False\n",
       "pay_num                  True\n",
       "pay_times                True\n",
       "last_month_traffic       True\n",
       "local_trafffic_month     True\n",
       "local_caller_time        True\n",
       "service1_caller_time     True\n",
       "service2_caller_time     True\n",
       "online_time             False\n",
       "age                      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data == 0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1808,
   "id": "b119a657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id 0\n",
      "gender 13\n",
      "last_pay_time 0\n",
      "pay_num 2\n",
      "pay_times 6\n",
      "last_month_traffic 205\n",
      "local_trafffic_month 68\n",
      "local_caller_time 92\n",
      "service1_caller_time 164\n",
      "service2_caller_time 101\n",
      "online_time 0\n",
      "age 12\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    count = 0\n",
    "    count = [count + 1 for x in data[col] if x == 0]\n",
    "    print(col+' '+str(sum(count)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1809,
   "id": "6ea9007d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=301, step=1)"
      ]
     },
     "execution_count": 1809,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index1 = (data[\"gender\"] == 0) & (data[\"age\"] == 0)\n",
    "index2 = (data[\"pay_num\"] == 0) | (data[\"pay_times\"] == 0)\n",
    "index4 = ((data[\"gender\"] != 0) | (data[\"age\"] != 0)) & ((data[\"pay_num\"] != 0) & (data[\"pay_times\"] != 0))\n",
    "index3 = ((data[\"gender\"] == 0) & (data[\"age\"] == 0)) | ((data[\"pay_num\"] == 0) | (data[\"pay_times\"] == 0))\n",
    "index3.index\n",
    "# print(index1)\n",
    "# print('index1类型：：',type(index1))\n",
    "# print(index2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1810,
   "id": "7a074e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     False\n",
      "1     False\n",
      "2     False\n",
      "3     False\n",
      "4     False\n",
      "5     False\n",
      "6     False\n",
      "7     False\n",
      "8     False\n",
      "9     False\n",
      "10     True\n",
      "11    False\n",
      "12    False\n",
      "13    False\n",
      "14    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(index3.head(15))\n",
    "# print(data[index3])\n",
    "# data[index3].index\n",
    "data = data.drop(data[index3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1811,
   "id": "3cb96bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 12)"
      ]
     },
     "execution_count": 1811,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1812,
   "id": "9a2ef8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 1812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.duplicated()\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1813,
   "id": "d87e72b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\freedong\\zhuzhu\\FullStackEngineer2025\\718BI_Data_Visualization_python_vue\\env\\Lib\\site-packages\\IPython\\core\\displayhook.py:292: UserWarning: Output cache limit (currently 1000 entries) hit.\n",
      "Flushing oldest 200 entries.\n",
      "  warn('Output cache limit (currently {sz} entries) hit.\\n'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.int64(2)"
      ]
     },
     "execution_count": 1813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 是用来检查 Pandas DataFrame 中 user_id 列是否有重复值，并统计重复的数量\n",
    "data.duplicated(['user_id']).sum()\n",
    "# data.duplicated(['user_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1814,
   "id": "9277c416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              user_id  gender        last_pay_time  pay_num  pay_times  \\\n",
      "0    Gx4sJzcQog01UhZL       1  2016-06-26 00:00:00   300.04          2   \n",
      "1    kEXrhTiug93DIcLG       1  2016-06-26 00:00:00   300.00          3   \n",
      "2    AouXr0EOUtSRdiYK       2  2016-06-19 00:00:00    50.00          4   \n",
      "3    Yds7U30hnRZDiLtb       1  2016-06-16 00:00:00   100.00          1   \n",
      "4    OFDTSXrhN9Q2mbVw       1  2016-06-27 00:00:00  1000.03         12   \n",
      "..                ...     ...                  ...      ...        ...   \n",
      "296  qpXPSkahTJ4QnKCO       1  2016-06-19 00:00:00   200.07          3   \n",
      "297  LPdyxMrDVoa4K5cC       1  2016-06-24 00:00:00    50.00          1   \n",
      "298  pBUMdi2P8NhTYcj3       1  2016-06-30 00:00:00    30.00          1   \n",
      "299  5aNKrc6jFdfZvqus       1  2016-06-04 00:00:00   130.00          4   \n",
      "300  Qew6MoZPA9rcCyqV       1  2016-07-05 00:00:00  1000.00         10   \n",
      "\n",
      "     last_month_traffic  local_trafffic_month  local_caller_time  \\\n",
      "0           4096.000000           1392.038508         108.100000   \n",
      "1              0.000000          62852.509718         240.100000   \n",
      "2              0.000000           1935.242104          27.666667   \n",
      "3             37.336425            988.561075          89.900000   \n",
      "4           3305.741127              0.000000           0.000000   \n",
      "..                  ...                   ...                ...   \n",
      "296            0.000000           2357.521831          78.650000   \n",
      "297            0.000000          25244.866906           3.916667   \n",
      "298            0.000000          12628.949589           0.216667   \n",
      "299            0.000000          13386.708108           8.183333   \n",
      "300          800.000000              0.000000           0.000000   \n",
      "\n",
      "     service1_caller_time  service2_caller_time  online_time  age  \n",
      "0                0.000000            564.366667           85   31  \n",
      "1              355.166667              0.000000           10   30  \n",
      "2                0.000000             98.416667           12   25  \n",
      "3               74.483333            121.833333          134   44  \n",
      "4               35.100000            496.733333           84   31  \n",
      "..                    ...                   ...          ...  ...  \n",
      "296              0.000000            160.133333           94   33  \n",
      "297            147.350000              0.000000           18   22  \n",
      "298              0.216667              0.000000           18   20  \n",
      "299              0.000000             68.250000            4   18  \n",
      "300              2.383333              0.000000          134   60  \n",
      "\n",
      "[286 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "data = data.drop_duplicates(['user_id'])\n",
    "print(data)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1815,
   "id": "114fff0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 12)"
      ]
     },
     "execution_count": 1815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1816,
   "id": "e678b5ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\498806042.py:2: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  gender = pd.value_counts(data['gender'])\n",
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\498806042.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  age = pd.value_counts(data['age'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置字体为黑体\n",
    "gender = pd.value_counts(data['gender'])\n",
    "# print(gender)\n",
    "age = pd.value_counts(data['age'])\n",
    "# print(age)\n",
    "plt.bar(gender.index,gender,width=0.5,tick_label=['男','女'],color='c')\n",
    "plt.xlabel('性别',fontsize=12)\n",
    "plt.ylabel('人数',fontsize=12)\n",
    "plt.title('性别-人数统计图',fontsize=16)\n",
    "plt.savefig('性别-人数统计图',dpi=128)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1817,
   "id": "aa877fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = age.sort_index()\n",
    "# print(age)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(age.index,age)\n",
    "plt.xticks(range(0, 80, 5),fontsize=12)\n",
    "plt.yticks(range(2,20),fontsize=12)\n",
    "plt.grid(ls=':',alpha=0.8)\n",
    "plt.xlabel('年龄',fontsize=14)\n",
    "plt.ylabel('人数',fontsize=14)\n",
    "plt.title('年龄-人数统计图',fontsize=20)\n",
    "plt.fill_between(age.index, age, color='c', alpha=0.3)\n",
    "plt.savefig('年龄-人数统计图',dpi=128)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b60ab2",
   "metadata": {},
   "source": [
    "## 样本提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1818,
   "id": "328051d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_select = data[['user_id','pay_num','pay_times','last_pay_time']]\n",
    "# print(data_select.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1819,
   "id": "63862047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>pay_num</th>\n",
       "      <th>pay_times</th>\n",
       "      <th>last_pay_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  pay_num  pay_times        last_pay_time\n",
       "0  Gx4sJzcQog01UhZL   300.04          2  2016-06-26 00:00:00\n",
       "1  kEXrhTiug93DIcLG   300.00          3  2016-06-26 00:00:00\n",
       "2  AouXr0EOUtSRdiYK    50.00          4  2016-06-19 00:00:00\n",
       "3  Yds7U30hnRZDiLtb   100.00          1  2016-06-16 00:00:00\n",
       "4  OFDTSXrhN9Q2mbVw  1000.03         12  2016-06-27 00:00:00"
      ]
     },
     "execution_count": 1819,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1820,
   "id": "8f6d03fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
       "      <th>最后一次消费时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               用户id     消费金额  消费次数             最后一次消费时间\n",
       "0  Gx4sJzcQog01UhZL   300.04     2  2016-06-26 00:00:00\n",
       "1  kEXrhTiug93DIcLG   300.00     3  2016-06-26 00:00:00\n",
       "2  AouXr0EOUtSRdiYK    50.00     4  2016-06-19 00:00:00\n",
       "3  Yds7U30hnRZDiLtb   100.00     1  2016-06-16 00:00:00\n",
       "4  OFDTSXrhN9Q2mbVw  1000.03    12  2016-06-27 00:00:00"
      ]
     },
     "execution_count": 1820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select.columns = ['用户id','消费金额','消费次数','最后一次消费时间']\n",
    "data_select.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1821,
   "id": "007d4637",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(data_select['用户id'])\n",
    "# data_select.index = data_select['用户id']\n",
    "# print(data_select['用户id'])\n",
    "\n",
    "# data_select = data_select.drop(columns=['用户id'])\n",
    "# print(data_select.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1822,
   "id": "7529ce22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-07-20 00:00:00\n",
      "2016-06-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "exdata_date = datetime(2016,7,20)\n",
    "start_date = datetime(2016,6,1)\n",
    "print(exdata_date)\n",
    "print(start_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1823,
   "id": "4f70e82b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\4081126291.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_select['最后一次消费时间'] = pd.to_datetime(data_select['最后一次消费时间'])\n",
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\4081126291.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_select['R(最后一次消费距提数日时间)'] = exdata_date - data_select['最后一次消费时间']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
       "      <th>最后一次消费时间</th>\n",
       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>31 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>34 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               用户id     消费金额  消费次数   最后一次消费时间 R(最后一次消费距提数日时间)\n",
       "0  Gx4sJzcQog01UhZL   300.04     2 2016-06-26         24 days\n",
       "1  kEXrhTiug93DIcLG   300.00     3 2016-06-26         24 days\n",
       "2  AouXr0EOUtSRdiYK    50.00     4 2016-06-19         31 days\n",
       "3  Yds7U30hnRZDiLtb   100.00     1 2016-06-16         34 days\n",
       "4  OFDTSXrhN9Q2mbVw  1000.03    12 2016-06-27         23 days"
      ]
     },
     "execution_count": 1823,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select['最后一次消费时间'] = pd.to_datetime(data_select['最后一次消费时间'])\n",
    "# print(data_select['最后一次消费时间'].head())\n",
    "data_select['R(最后一次消费距提数日时间)'] = exdata_date - data_select['最后一次消费时间']\n",
    "data_select.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1824,
   "id": "9c2496a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 1824,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select['R(最后一次消费距提数日时间)'][0].days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1825,
   "id": "803df484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24, 24, 31, 34, 23, 28, 39, 22, 38, 25]"
      ]
     },
     "execution_count": 1825,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_R = []\n",
    "\n",
    "for i in data_select['R(最后一次消费距提数日时间)']:\n",
    "    key_R.append(i.days)\n",
    "\n",
    "key_R[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1826,
   "id": "2873019b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   25 days\n",
       "1   25 days\n",
       "2   18 days\n",
       "3   15 days\n",
       "4   26 days\n",
       "5   21 days\n",
       "6   10 days\n",
       "7   27 days\n",
       "8   11 days\n",
       "9   24 days\n",
       "Name: 最后一次消费时间, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 1826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period_day = data_select['最后一次消费时间'] - start_date\n",
    "period_day[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1827,
   "id": "08a00bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2]\n",
      "286\n",
      "**************************************************************************************************************\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "from math import ceil\n",
    "\n",
    "period_month = []\n",
    "\n",
    "for i in period_day:\n",
    "    period_month.append(ceil(i.days / 30))\n",
    "\n",
    "print(period_month)\n",
    "print(len(period_month))\n",
    "print('*'*110)\n",
    "\n",
    "for i in range(0,len(period_month)):\n",
    "    if period_month[i] == 0:\n",
    "        period_month[i] = 1\n",
    "\n",
    "print(period_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1828,
   "id": "608ec9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\1707955642.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_select['F(月均消费次数)'] = data_select['消费次数'] / period_month\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
       "      <th>最后一次消费时间</th>\n",
       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "      <th>F(月均消费次数)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>31 days</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>34 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4qHSn3dkPzJTAjoG</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>28 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tXkjbzpTsZcxYPKG</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-11</td>\n",
       "      <td>39 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ro43b68MustgPyOR</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>22 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18e2VC0IJ7SkcKzF</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>38 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>YCEo95zSZ08IJ3PW</td>\n",
       "      <td>120.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cfGAgMWTJLt09VDs</td>\n",
       "      <td>101.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>km0xTQGpIYZuXWyL</td>\n",
       "      <td>200.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-23</td>\n",
       "      <td>27 days</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>d9L37fAwopQkWPYh</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>28 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UXewZPdJbBiycY8Q</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Z5igARInaGBkjYdm</td>\n",
       "      <td>1477.60</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gH7nkfV2a0cKPQT6</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>38 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>wnYh1QfSNBcGMTJ5</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>zPhEjLIWi3dKV6GU</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JzrYtgsjF45bBun1</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rL402wVDpGPBfC8q</td>\n",
       "      <td>300.05</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>35 days</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CognwxZ2aJfv03c4</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>QnU8PiaY63eAVlDr</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>xPRY6725k90KdyjW</td>\n",
       "      <td>80.50</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>qLyPkjf86sZdEtNg</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-18</td>\n",
       "      <td>2 days</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>IViSctryC8lTBvDj</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>6 days</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NAQc9BvmlKFjTgoC</td>\n",
       "      <td>100.05</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>D8Y21qN945kBieLd</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>44 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>jzCO0UbDyAhJgeGN</td>\n",
       "      <td>150.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-20</td>\n",
       "      <td>30 days</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8qwBzScDaQdP61bL</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NftPFxHg3kdcAm6n</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>22 days</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                用户id     消费金额  消费次数   最后一次消费时间 R(最后一次消费距提数日时间)  F(月均消费次数)\n",
       "0   Gx4sJzcQog01UhZL   300.04     2 2016-06-26         24 days        2.0\n",
       "1   kEXrhTiug93DIcLG   300.00     3 2016-06-26         24 days        3.0\n",
       "2   AouXr0EOUtSRdiYK    50.00     4 2016-06-19         31 days        4.0\n",
       "3   Yds7U30hnRZDiLtb   100.00     1 2016-06-16         34 days        1.0\n",
       "4   OFDTSXrhN9Q2mbVw  1000.03    12 2016-06-27         23 days       12.0\n",
       "5   4qHSn3dkPzJTAjoG    30.00     1 2016-06-22         28 days        1.0\n",
       "6   tXkjbzpTsZcxYPKG    50.00     1 2016-06-11         39 days        1.0\n",
       "7   ro43b68MustgPyOR   200.00     2 2016-06-28         22 days        2.0\n",
       "8   18e2VC0IJ7SkcKzF   200.00     2 2016-06-12         38 days        2.0\n",
       "9   YCEo95zSZ08IJ3PW   120.00     2 2016-06-25         25 days        2.0\n",
       "11  cfGAgMWTJLt09VDs   101.00     2 2016-06-29         21 days        2.0\n",
       "12  km0xTQGpIYZuXWyL   200.00     3 2016-06-23         27 days        3.0\n",
       "13  d9L37fAwopQkWPYh    50.00     1 2016-06-22         28 days        1.0\n",
       "14  UXewZPdJbBiycY8Q   100.00     1 2016-06-27         23 days        1.0\n",
       "15  Z5igARInaGBkjYdm  1477.60    10 2016-06-18         32 days       10.0\n",
       "16  gH7nkfV2a0cKPQT6    50.00     1 2016-06-12         38 days        1.0\n",
       "17  wnYh1QfSNBcGMTJ5    30.00     1 2016-06-29         21 days        1.0\n",
       "18  zPhEjLIWi3dKV6GU   100.00     1 2016-06-25         25 days        1.0\n",
       "19  JzrYtgsjF45bBun1    40.00     2 2016-06-26         24 days        2.0\n",
       "20  rL402wVDpGPBfC8q   300.05     4 2016-06-15         35 days        4.0\n",
       "21  CognwxZ2aJfv03c4   100.00     1 2016-06-25         25 days        1.0\n",
       "22  QnU8PiaY63eAVlDr   100.00     1 2016-06-18         32 days        1.0\n",
       "23  xPRY6725k90KdyjW    80.50     2 2016-06-26         24 days        2.0\n",
       "24  qLyPkjf86sZdEtNg    50.00     1 2016-07-18          2 days        0.5\n",
       "25  IViSctryC8lTBvDj     0.07     1 2016-07-14          6 days        0.5\n",
       "26  NAQc9BvmlKFjTgoC   100.05     3 2016-06-18         32 days        3.0\n",
       "28  D8Y21qN945kBieLd    50.00     1 2016-06-06         44 days        1.0\n",
       "29  jzCO0UbDyAhJgeGN   150.00     2 2016-06-20         30 days        2.0\n",
       "30  8qwBzScDaQdP61bL    60.00     4 2016-06-27         23 days        4.0\n",
       "31  NftPFxHg3kdcAm6n    30.00     1 2016-06-28         22 days        1.0"
      ]
     },
     "execution_count": 1828,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select['F(月均消费次数)'] = data_select['消费次数'] / period_month\n",
    "data_select.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1829,
   "id": "d4980581",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Desktop-A10\\AppData\\Local\\Temp\\ipykernel_27920\\1287739291.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_select['M(月均消费金额)'] = data_select['消费金额'] / period_month\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
       "      <th>最后一次消费时间</th>\n",
       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "      <th>F(月均消费次数)</th>\n",
       "      <th>M(月均消费金额)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kEXrhTiug93DIcLG</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>31 days</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>34 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1000.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4qHSn3dkPzJTAjoG</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>28 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tXkjbzpTsZcxYPKG</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-11</td>\n",
       "      <td>39 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ro43b68MustgPyOR</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>22 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18e2VC0IJ7SkcKzF</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>38 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>YCEo95zSZ08IJ3PW</td>\n",
       "      <td>120.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cfGAgMWTJLt09VDs</td>\n",
       "      <td>101.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>km0xTQGpIYZuXWyL</td>\n",
       "      <td>200.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-23</td>\n",
       "      <td>27 days</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>d9L37fAwopQkWPYh</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>28 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UXewZPdJbBiycY8Q</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Z5igARInaGBkjYdm</td>\n",
       "      <td>1477.60</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1477.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gH7nkfV2a0cKPQT6</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>38 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>wnYh1QfSNBcGMTJ5</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>zPhEjLIWi3dKV6GU</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JzrYtgsjF45bBun1</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rL402wVDpGPBfC8q</td>\n",
       "      <td>300.05</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>35 days</td>\n",
       "      <td>4.0</td>\n",
       "      <td>300.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CognwxZ2aJfv03c4</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>QnU8PiaY63eAVlDr</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>xPRY6725k90KdyjW</td>\n",
       "      <td>80.50</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>qLyPkjf86sZdEtNg</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-18</td>\n",
       "      <td>2 days</td>\n",
       "      <td>0.5</td>\n",
       "      <td>25.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>IViSctryC8lTBvDj</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>6 days</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NAQc9BvmlKFjTgoC</td>\n",
       "      <td>100.05</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>32 days</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>D8Y21qN945kBieLd</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>44 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>jzCO0UbDyAhJgeGN</td>\n",
       "      <td>150.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-20</td>\n",
       "      <td>30 days</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8qwBzScDaQdP61bL</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23 days</td>\n",
       "      <td>4.0</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NftPFxHg3kdcAm6n</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>22 days</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                用户id     消费金额  消费次数   最后一次消费时间 R(最后一次消费距提数日时间)  F(月均消费次数)  \\\n",
       "0   Gx4sJzcQog01UhZL   300.04     2 2016-06-26         24 days        2.0   \n",
       "1   kEXrhTiug93DIcLG   300.00     3 2016-06-26         24 days        3.0   \n",
       "2   AouXr0EOUtSRdiYK    50.00     4 2016-06-19         31 days        4.0   \n",
       "3   Yds7U30hnRZDiLtb   100.00     1 2016-06-16         34 days        1.0   \n",
       "4   OFDTSXrhN9Q2mbVw  1000.03    12 2016-06-27         23 days       12.0   \n",
       "5   4qHSn3dkPzJTAjoG    30.00     1 2016-06-22         28 days        1.0   \n",
       "6   tXkjbzpTsZcxYPKG    50.00     1 2016-06-11         39 days        1.0   \n",
       "7   ro43b68MustgPyOR   200.00     2 2016-06-28         22 days        2.0   \n",
       "8   18e2VC0IJ7SkcKzF   200.00     2 2016-06-12         38 days        2.0   \n",
       "9   YCEo95zSZ08IJ3PW   120.00     2 2016-06-25         25 days        2.0   \n",
       "11  cfGAgMWTJLt09VDs   101.00     2 2016-06-29         21 days        2.0   \n",
       "12  km0xTQGpIYZuXWyL   200.00     3 2016-06-23         27 days        3.0   \n",
       "13  d9L37fAwopQkWPYh    50.00     1 2016-06-22         28 days        1.0   \n",
       "14  UXewZPdJbBiycY8Q   100.00     1 2016-06-27         23 days        1.0   \n",
       "15  Z5igARInaGBkjYdm  1477.60    10 2016-06-18         32 days       10.0   \n",
       "16  gH7nkfV2a0cKPQT6    50.00     1 2016-06-12         38 days        1.0   \n",
       "17  wnYh1QfSNBcGMTJ5    30.00     1 2016-06-29         21 days        1.0   \n",
       "18  zPhEjLIWi3dKV6GU   100.00     1 2016-06-25         25 days        1.0   \n",
       "19  JzrYtgsjF45bBun1    40.00     2 2016-06-26         24 days        2.0   \n",
       "20  rL402wVDpGPBfC8q   300.05     4 2016-06-15         35 days        4.0   \n",
       "21  CognwxZ2aJfv03c4   100.00     1 2016-06-25         25 days        1.0   \n",
       "22  QnU8PiaY63eAVlDr   100.00     1 2016-06-18         32 days        1.0   \n",
       "23  xPRY6725k90KdyjW    80.50     2 2016-06-26         24 days        2.0   \n",
       "24  qLyPkjf86sZdEtNg    50.00     1 2016-07-18          2 days        0.5   \n",
       "25  IViSctryC8lTBvDj     0.07     1 2016-07-14          6 days        0.5   \n",
       "26  NAQc9BvmlKFjTgoC   100.05     3 2016-06-18         32 days        3.0   \n",
       "28  D8Y21qN945kBieLd    50.00     1 2016-06-06         44 days        1.0   \n",
       "29  jzCO0UbDyAhJgeGN   150.00     2 2016-06-20         30 days        2.0   \n",
       "30  8qwBzScDaQdP61bL    60.00     4 2016-06-27         23 days        4.0   \n",
       "31  NftPFxHg3kdcAm6n    30.00     1 2016-06-28         22 days        1.0   \n",
       "\n",
       "    M(月均消费金额)  \n",
       "0     300.040  \n",
       "1     300.000  \n",
       "2      50.000  \n",
       "3     100.000  \n",
       "4    1000.030  \n",
       "5      30.000  \n",
       "6      50.000  \n",
       "7     200.000  \n",
       "8     200.000  \n",
       "9     120.000  \n",
       "11    101.000  \n",
       "12    200.000  \n",
       "13     50.000  \n",
       "14    100.000  \n",
       "15   1477.600  \n",
       "16     50.000  \n",
       "17     30.000  \n",
       "18    100.000  \n",
       "19     40.000  \n",
       "20    300.050  \n",
       "21    100.000  \n",
       "22    100.000  \n",
       "23     80.500  \n",
       "24     25.000  \n",
       "25      0.035  \n",
       "26    100.050  \n",
       "28     50.000  \n",
       "29    150.000  \n",
       "30     60.000  \n",
       "31     30.000  "
      ]
     },
     "execution_count": 1829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_select['M(月均消费金额)'] = data_select['消费金额'] / period_month\n",
    "data_select.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1830,
   "id": "11402ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据已保存到 移动公司客户信息预处理.xlsx\n"
     ]
    }
   ],
   "source": [
    "# 去掉列标题空白\n",
    "data_select = data_select.rename(columns=lambda x: x.strip())\n",
    "# data_select.head(10)\n",
    "# 保存数据\n",
    "output_file_path = '移动公司客户信息预处理.xlsx'\n",
    "data_select.to_excel(output_file_path, index=False)\n",
    "print(f\"数据已保存到 {output_file_path}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
